Explore the capabilities of Amazon SageMaker to effectively train, deploy, and manage machine learning and deep learning models in AWS with complete and thoroughly explained hands-on examples. With 80 proven recipes, this step-by-step guide will help you build the skills necessary to contribute to real-life ML requirements and projects.
Über den Autor
Joshua Arvin Lat is the Chief Technology Officer (CTO) of NuWorks Interactive Labs, Inc. He previously served as the CTO of three Australian-owned companies and also served as the director for software development and engineering for multiple e-commerce start-ups in the past, which allowed him to be more effective as a leader. Years ago, he and his team won first place in a global cybersecurity competition with their published research paper. He is also an AWS Machine Learning Hero and has shared his knowledge at several international conferences, discussing practical strategies on machine learning, engineering, security, and management.
Klappentext
A step-by-step solution-based guide to preparing building, training, and deploying high-quality machine learning models with Amazon SageMakernnnKey Features:Perform ML experiments with built-in and custom algorithms in SageMakernExplore proven solutions when working with TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learnnUse the different features and capabilities of SageMaker to automate relevant ML processesnnnnnBook Deillegalscription:nAmazon SageMaker is a fully managed machine learning (ML) service that helps data scientists and ML practitioners manage ML experiments. In this book, you'll use the different capabilities and features of Amazon SageMaker to solve relevant data science and ML problems.nnnThis step-by-step guide features 80 proven recipes designed to give you the hands-on machine learning experience needed to contribute to real-world experiments and projects. You'll cover the algorithms and techniques that are commonly used when training and deploying NLP, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning libraries and frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers in Amazon SageMaker. You'll also learn how to use SageMaker Clarify, SageMaker Model Monitor, SageMaker Debugger, and SageMaker Experiments to debug, manage, and monitor multiple ML experiments and deployments. Moreover, you'll have a better understanding of how SageMaker Feature Store, Autopilot, and Pipelines can meet the specific needs of data science teams.nnnBy the end of this book, you'll be able to combine the different solutions you've learned as building blocks to solve real-world ML problems.nnnWhat You Will Learn:Train and deploy NLP, time series forecasting, and computer vision models to solve different business problemsnPush the limits of customization in SageMaker using custom container imagesnUse AutoML capabilities with SageMaker Autopilot to create high-quality modelsnWork with effective data analysis and preparation techniquesnExplore solutions for debugging and managing ML experiments and deploymentsnDeal with bias detection and ML explainability requirements using SageMaker ClarifynAutomate intermediate and complex deployments and workflows using a variety of solutionsnnnnnWho this book is for:nThis book is for developers, data scientists, and machine learning practitioners interested in using Amazon SageMaker to build, analyze, and deploy machine learning models with 80 step-by-step recipes. All you need is an AWS account to get things running. Prior knowledge of AWS, machine learning, and the Python programming language will help you to grasp the concepts covered in this book more effectively.
